Author: Michael Davison

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Insights

So, what exactly is a DMP?

EDITOR’S NOTE: This article is about how data management platforms (DMPs) can assist decision-makers in organizing their data in ways leading to strategic insights. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation DMP, specifically to make it easy for leaders to collect and manage data to get to insights faster. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design.

A data management platform, or DMP, imports, stores and compiles customer or target audience data from various sources and makes it actionable. It can ingest, sanitize, sort and format data. Most importantly, it can analyze and segment this data and present it in a visual format that is easily understood and made use of by executive decision makers.

In the AdTech/Martech world, there’s a common misconception that DMPs are somehow exclusive to the digital advertising ecosystem where DMPs produce audience segments that are syndicated to external ad targeting and content delivery platforms and compared across channels. The reality is that everyone from toy stores to hedge funds and even government agencies are employing DMPs for internal data management. Finance firms may use a DMP for data forensics. Retail giants are increasingly employing DMPs as 1:1 data engines that personalize the e-commerce experience with recommendation engines and displays based upon rich user profiles. Enterprise organizations and SMBs alike utilize DMPs for non-advertising/marketing tasks such as aggregation of scraped and purchased data sets, business intelligence, product management and inventory. In fact, DMPs utilizing AI have been replacing traditional supply chain management departments at a rapid pace in 2017.

Both B2B and B2C organizations leverage a DMP to understand customer audiences based upon conversion, engagement and purchase rates and to target audiences with personalized and therefore more effective messaging. A typical B2B use case involves the matching and correlation of 1st party data with 3rd party data for look alike modeling which provides channel clarity and really enables business to build profiles at a company level that groups together individuals associated with or employed by organizations that are sales targets.

The first thing an organization that is considering purchasing a DMP should do is establish what the process and flow will look like for the intake of data from multiple sources stored in various locations. Run a test with a month’s worth of data. See what kind of issues you encounter getting your data ingested and normalized. Concurrent to establishing a process for input, you want to understand your own business goals and ultimately identify what is of value to your team’s mission. This could be new registrations and cohort starts and the VPA Value Per Acquisition or CLV Customer Lifetime Value. An organization could simply want to understand what the impact of their newly refurbished, responsive website is on their e-commerce and what platform, device or browser are their most avid customers using. Prioritize the top 4 or 5 data points and make sure these are stood up as part of the initial integration.

In the digital advertising industry, it can be a challenge to differentiate between a DMP and a demand-side platform (DSP) as the lines are continually blurred. Some DSPs have begun to offer DMP functionality to inform their ad purchasing and to avoid lag and integration problems that typically result from today’s fragmented martech/adtech stack. Some have morphed into SSPs that integrate with multiple DMPs while simultaneously offering their own DMP service. Bottom line though: DMPs store and process data, sort it and provide context and insight. Beyond that, they don’t function as an exchange or DSP. A DMP does not coordinate programmatic ad campaigns for you.

A DMP can be used to map different SourceIDs and cookie IDs across the ecosystem. This is major problem that needs resolution in that an industry standard does not exist. So, you have Ad networks, mobile exchanges, middle man measurement tools, data management platforms, fraud vendors, SSPs, agency trading desks and DSPs all using various IDs to track transactions. Attribution can get quite complicated.

A good DMP can cleanse and process structured and unstructured data alike and generate visual analytics for the data from multiple departments, programs and campaigns. Ideally, the data becomes actionable and decisions become validated, justified and quantified by the insights produced. Data is compartmentalized and segments may be produced. As we approach 2018, I can’t imagine recommending a DMP that is not 100% cloud based, as it needs to scale. Similarly, it should possess an intelligent layer of machine learning. A good DMP offers their users the option of either API stream or S3 data bucket upload, whichever is preferred by the customer.

Clearly the point is to manage one’s data but also to merge it and make sense of it. Ultimately, a DMP should enable the monetization of an organization’s data. A good DMP will create one holistic view of all data within an organization. Synaptik is a DMP that is flexible enough to address you strategic data needs across a number of organizational functions: Finance, Analytics, IT, Marketing, Operations and Customer Relation Management, among others. Synaptik’s advanced intelligent layer can even draw correlations between the different data. While most businesses are overwhelmed by the sheer volume of data that they are failing to leverage, others may be intimidated by the thought of purchasing a DMP because they don’t think they have the capacity, or the technical DNA in house to take this kind of thing on. Well, a DMP is supposed to minimize both labor and angst and should come with frictionless on-boarding and attentive support for rule mapping and customization. The DMP staff should be falling over themselves to meet your terms. Pointing customers to a rabbit hole of self help technical article links and leaving it up to the customer to figure out how best to get things up and running is not acceptable. The DMP you select should be intuitive enough for you to figure out how to configure it on your own once it’s been deployed. Lastly, a good DMP should feel be agnostic and customizable.

At True Interaction, we pride ourselves in our Digital Transformation Services along with our Data Intelligence acumen. Please schedule a time to have a discovery conversation today.

by David Sheihan Hunter Lindez

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Insights

Here Comes Fake Data!

EDITOR’S NOTE: This is a guest article authored by Geber Consulting. The original article was featured here. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design.

Continue reading “Here Comes Fake Data!”
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Insights

Social Media Principles, Evolution and Future

Social Media Principles

Marketing is a process as old a business itself. However, the invention and proliferation of digital advertising, often through social media channels, has transformed the ways in which brands and their marketing agencies interact with current and potential customers.

Rich Taylor, a Chief Marketing Officer (CMO) and executive-level consultant with three decades of industry experience sat down with the True Interaction team to share his expertise on how companies leverage the power of social media to better market their products. Below are his insights into the changing digital advertising space.

 

TI: What do you see as the key principles of effective social media use by brands and agencies?

It’s not advertising. “There has been a push from all the social channels to monetize social (media), and in that regard they get advertisers to pay for eyeballs. But all the research shows that when you run advertising on social channels it does not work well. People are not tuning in to social media to watch ads. What they want is content marketing…don’t create ads; engage your audience.”

Focus on the service component. “Social media is a chance to interact with your customers, to thank them for their purchases and positive comments, address a concern or issue they have promptly. That’s what people want. People want to know that you are paying attention, listening and that you will respond.”

Personify your use of social media. “Social (media) is about interacting with friends and family or colleagues and other professionals. There is an inherently personal element to it. Having a voice or personality matters. You may be a great big successful brand or a small startup brand, but if you haven’t established a tone, a personality, a voice, a manner of interacting, you are not going to get the type of engagement you are really looking for.”

 

TI: True Interaction helps companies across a number of industries in automating their data ingestion, management and visualization processes. How do you see companies successfully using automation in the social media space?

“The trend is towards convenience. ‘How do I interact and make it very easy for my customer?’ (For example) Domino’s uses chatbots and artificial intelligence to address frequently asked questions and also to streamline orders…they created an automation mechanism within social media so that when you sent a pizza emoji or image it would trigger an order based on your profile. They are using automation and tools to say, ‘I have you engaged somewhere, Instagram, Twitter, Facebook, Google. Now, how do I take that interaction quickly and easily from our marketing group to our sales and customer service areas?’.”

“Also, digital personalization. The Amazon Reviews Effect of ‘other customers like you bought this’…There is a lot of technology and automation now being done to personalize the content you see based on your browsing history, your shopping history, what they know about you. And they gather that information from your email, from cookies, from a lot of different things. I think applying and using personal data is the challenge now for a lot of marketers.”

 

TI: Solutions like chatbots, digital personalization and machine learning may, at this time, be most attainable for more established, well resourced brands. What social media solutions would you recommend to smaller business with greater resource scarcity?

“The interesting thing that I am seeing is big companies like Walmart…who can afford to ingest very expensive and sophisticated shopper data and analytics programs….puts a lot of pressure on mom and pops and regional players. The great news is that nowadays, historic barriers to success have been the costs of IT infrastructure and systems…but what you are finding is that merchants vendors themselves are helping (small businesses)…American Express is an example. (American Express) recognizes that (small businesses) want to have access to what they are using their cards for. If AmEx has this capability, (a small business) does not have to be a Walmart that is going to build (their own system) or bring someone to build it for them. They can just talk to American Express or another company that is in the digital space who is now going to have the capability to do this work and that to not require a lot of resources to access.You just need a combination of access to the tool and bring on some talent or some consultants who can help you mine that data.”

 

TI: How has social media evolved during your time as a marketer?

Social Media 1.0: “Blasting out one-size-fits all ads to everybody.”

Social Media 2.0: “Traditional advertising methods…versioning content based on a segment of customers.”

Social Media 3.0: “Big data. I am going to be using big data, whether it’s shopping data or a combination of shopping data and search data to more precisely personalize the content to an individual and make it relevant to them. For example, retargeting ads. If you are on Zappos.com and you look for a pair of shoes, next time you log into Facebook, all of a sudden you see the shoes you were looking for being retargeted to you in the ad.”

 

TI: So what is the future of social media? In other words, what is Social Media 4.0?

“Thats a good question. I think we are starting to see a little fatigue with social media. You are not seeing new platforms pop up and succeed as fast as they did. I think at some point Facebook and Twitter are not the channels you tune into anymore….I am going to set up Rich Taylor’s network, and it is going to be my personalized list…I am going to build my own media and entertainment channel and it is going to be based on my habits and preferences for information.”

“People are spending a lot of time on Facebook right now, but that’s because Facebook is holding them hostage and they can’t get their content anywhere else…I think you are going to see social media going to get more focused on integrating with broadcast. How does broadcasting merge ultimately with digital? Broadcasting is the powerhouse in advertising, it’s the easiest form of entertainment, and I think that you are seeing a push towards more and more video content, easy to digest.”

“At some point, I will be watching my TV or a program on my tablet, and there might be a scroll or a ticker, like financial news, scrolling underneath with my social elements in it and I might see a quick blip or a picture or an image that might say, ‘you gotta see this!’ and then I might click on it, it will open up, and I will watch it.”

 

EDITOR’S NOTE: This article is about how to approach and think about social media and digital advertising. True Interaction built SYNAPTIK, our Data Management, Analytics, and Data Science Simulation Platform, specifically to make it easy to collect and manage core and alternative data for more meaningful data discovery. For more information or a demo, please visit us at https://synaptik.co/ or email us at hello@www.true.design.

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Data, Automation & AI Featured

AI and the Classroom: Machine Learning in Education

Situation

For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students’ homework, and provide feedback to the students (and the students’ parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a “one-size-fits-all” gauntlet of learning, not personalized to their abilities, needs, or learning context. I’m always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson:

“Why is there this assumption that we should educate children simply according to how old they are? It’s almost as if the most important thing that children have in common is their date of manufacture.”

But as the contemporary classroom has become more and more digitized, we’ve seen recent advances in AI and machine learning that are closing in on being able to finally address historical “hand-wrought” challenges – by not only collecting and analyzing data that students generate (such as e-learning log files) when they interact with digital learning systems, but by pulling in large swaths of data from other areas including demographic data of students, educator demographic and performance data, admissions and registration info, human resources information, and so forth.

Quick Review: What is Machine Learning?

Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look Machine learning works especially well prediction and estimation when the following are true:

-The inputs are well understood. (You have a pretty good idea of what is important but not how to combine them.)
-The output is well understood. (You know what you are trying to model.)
-Experience is available. (You have plenty of examples to train the data.)

The crucible of machine learning consists of capturing and maintaining a rich set of data, and bringing about the serendipitous state of knowledge discovery: the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. As long as the data flows, its application is endless, and we already see it everywhere, from Facebook algorithms to self-driving cars. Today, let’s examine machine learning and its implementation in the field of Education.

Application of Machine Learning in Education

Prediction

A few years ago, Sotiris Kotsiantis, mathematics professor at the University of Patras, Greece presented a novel case study describing the emerging field of educational data mining, where he explored using students’ key demographic characteristic data and grading data in a small number of written assignments as the data set for a machine learning regression method that can be used to predict a student’s future performance.

In a similar vein, GovHack, Australia’s largest open government and open data hackathon included several projects in the education space, including a project that aims to develop a prediction model that can be used by educators, schools, and policy makers to predict the risk of a student to drop out of school.

Springboarding from these two examples, IBM’s Chalapathy Neti recently shared IBM’s vision of Smart Classrooms: cloud-based learning systems that can help teachers identify students who are most at risk of dropping out, why they are struggling, as well as provide insight into the interventions needed to overcome their learning challenges:

The system could also couple a student’s goals and interests with data on their learning styles so that teachers can determine what type of content to give the student, and the best way to present it. Imagine an eighth grader who dreams of working in finance but struggles with quadratic and linear equations. The teacher would use this cognitive system to find out the students learning style and develop a plan that addresses their knowledge gaps.

Process efficiency: Scheduling, grading, organization

Elsewhere, several Machine Learning for Education ICML (international machine learning conference) workshops have explored novel machine learning applications designed to benefit the education community, such as:

-Learning analytics that build statistical models of student knowledge to provide computerized and personalized feedback on learning the students’ progress and their instructors
-Content analytics that organize and optimize content items like assessments, textbook sections, lecture videos, etc.
-Scheduling algorithms that search for an optimal and adapted teaching policy that helps students learn more efficiently
-Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading
-Cognitive psychology, where data mining is becoming a powerful tool to validate the theories developed in cognitive science and facilitate the development of new theories to improve the learning process and knowledge retention
-Active learning and experimental design, which adaptively select assessments and other learning resources for each student individually to enhance learning efficiency

Existing Platforms

Recently, digital education venture capitalist Tom Vander Ark shared 8 different areas where leading-edge platforms are already leveraging machine learning in education:

1. Content analytics that organize and optimize content modules:
a. Gooru , IBM Watson Content Analytics

2. Learning analytics that track student knowledge and recommend next steps:
a. Adaptive learning systems: DreamBox, ALEKS, Reasoning Mind, Knewton
b. Game-based learning: ST Math, Mangahigh

3. Dynamic scheduling matches students that need help with teachers that have time:
a. NewClassrooms uses learning analytics to schedule personalized math learning experiences.

Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading:
a. Pearson’s WriteToLearn and Turnitin’s Lightside can score essays and detect plagiarism.

5. Process intelligence tools analyze large amounts of structured and unstructured data, visualize workflows and identifying new opportunities:
a. BrightBytes Clarity reviews research and best practices, creates evidence-based frameworks, and provides a strength gap analysis.
b. Enterprise Resource Planning (ERP) systems like Jenzabar and IBM SPSS helps HigherEd institutions predict enrollment, improve financial aid, boost retention, and enhancing campus security.

6. Matching teachers and schools:
a. MyEdMatch and TeacherMatch are eHarmony for schools.

7. Predictive analytics and data mining to learn from expertise to:
a. Map patterns of expert teachers
b. Improve learning, retention, and application.

8. Lots of back office stuff:
a. EDULOG does school bus scheduling
b. Evolution , DietMaster.

Reflection

As the modern classroom becomes more and more digitized, we are able to gather myriad sets of data. The trick is, of course, being able to purpose it. The prize at heart of machine learning is knowledge discovery, the process of parsing through the deluge of Big Data, identifying meaningful patterns within it, and transforming it into a structured knowledge base for future use. In this article, we’ve seen examples utilizing machine learning in the education sector for prediction, scheduling, grading, and organization. We’ve also listed existing education-related platforms that use a machine learning component.

What does it mean to me?

Big Data have swept into every industry and business function and are now an important factor in production, alongside labor and capital. In a decision making system, the bigger the data, the higher the likelihood is of making good decisions. The time is now for organizations, in education or otherwise, to research how a cost-efficient machine learning component can transform your operational output. For more information, Check out this detailed guide by Jesse Miller on the amazing benefits of technology in the classroom and suggestions on ways to incorporate technology in the classroom.

“Parents are continually exposed to new technology via their children. Whether it be iPad App usage tricks, to the advent of robotics competitions, and perhaps now “new ways of thinking” as a result of interaction with Machine Learning based educational environments. Siloed educational content may give way to a topology of learning experiences.” O. Liam Wright – CEO, True Interaction

True Interaction produces custom full-stack end-to-end technology solutions across web, desktop and mobile, integrating multiple data sources to create a customized data solution. True Interaction can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit the specific school and or district need. True Interaction pulls together disparate data sources, fuses together disconnected silos, and does exactly what it takes for school data systems to operate with high levels of efficiency and efficacy, ultimately leading to improved student achievement outcomes.

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Insights

Forget Your Development Team – is Your Business Agile?

Situation

In my last article, I interviewed business strategy consultant Michael Farmer of Farmer & Company regarding his new book, Madison Avenue Manslaughter, which details the plight of advertising agencies and their deteriorating situation today brought about by several paradigm shifts, including the shift from commissions to fees, brand globalization, the rise of holding companies, client obsession with shareholder value, and the digital and Internet revolutions. In the interview, I touched upon a quote by Albert Einstein:

We can’t solve problems by using the same kind of thinking we used when we created them.

While Einstein’s words most definitely apply to the trend in advertising agencies as detailed in Mr. Farmers book, let’s put away the magnifying glass, pull back for a moment, and explore business at large.

First of all, the average lifespan of an S&P 500 company has decreased from 61 years in 1958 to 27 years in 1980, to just 18 years now, and that number is diminishing as I write this. On average, an S&P 500 company is now being replaced about once every two weeks. And the churn rate of companies has been accelerating over time.

Comparing 1955 Fortune 500 companies to 2015 Fortune 500 (ranked by total revenues), there are only 61 companies that appear on both lists. Nearly 88% of the companies from 1955 have either gone bankrupt, merged with (or were acquired by) another firm, or they still exist but have fallen from the top 500. In other words, only 12.2% of the Fortune 500 companies in 1955 were still on the list 60 years later in 2015. Most of the 1955 companies on the list are unrecognizable today: Armstrong Rubber, Cone Mills, Hines Lumber, Pacific Vegetable Oil, and Riegel Textile. Today, successful companies need to explore new products, markets, and business models more frequently in order to continuously renew their advantage. According to BCG Perspectives,

“…companies face circumstances that change more rapidly and unpredictably than ever before because of technological advances and other factors. As a result, companies need to constantly renew their advantage, increasing the speed at which they shift resources among products and business units. Second, market share is no longer a direct predictor of sustained performance.”


Source

Defined by reduced time between innovation and adoption, increased market unpredictability, and reduced importance of market share, our modern business era has unveiled new drivers of competitive advantage – one of the most important being: the ability to adapt to changing circumstances or to shape them. This echoes Disney CEO Bob Iger’s famous quote: “The riskiest thing we can do is just maintain the status quo.” In a recent study of more than 900 business leaders, 93% responded that they “have completed, are planning, or are in the midst of a business transformation”. Really, what we are seeing is that “business transformation” isn’t something that is undergone once or even periodically – business transformation is becoming a continuous process.

Indeed today, businesses at large – not just their creative and development silos – benefit from operating in an Agile manner, most importantly in the area of responding to change over following a plan. Consider the words of Christa Carone, chief marketing officer for Xerox:

“Where we are right now as an enterprise, we would actually say there is no start and stop because the market is changing, evolving so rapidly. We always have to be aligning our business model with those realities in the marketplace.”

Solution

The situation in business today inevitably begs the question: “Where will your business be in 20 or even 10 years? Statistically, 9 of 10 people who are reading this are working for an organization that will NOT stand the test of time. But the good news that I’ve blogged about in the past is that progressive businesses that take the technology leap and invest in the future will reap tremendous gains over their less progressive peers. With that in mind, ALL SMBs should take the time to reassess the value of their business processes and technology solutions as soon as possible.

Need help determining the right solution? Consider these 9 criteria:

1. How easy and intuitive is the user interface?

– Affordance Visually, the UI has clues that indicate what it is going to do. Users don’t have to experiment or deduce the interaction. The affordances are based on real-world experiences or standard UI conventions.

– Expectation Functionally, the UI delivers the expected, predictable results, with no surprises. Users don’t have to experiment or deduce the effect. The expectations are based on labels, real-world experiences, or standard UI conventions.

-Efficiency The UI enables users to perform an action with a minimum amount of effort. If the intention is clear, the UI delivers the expected results the first time so that users don’t have to repeat the action (perhaps with variations) to get what they want.

-Responsiveness The UI gives clear, immediate feedback to indicate that the action is happening, and was either successful or unsuccessful.

-Forgiveness If users make a mistake, either the right thing happens anyway or they can fix or undo the action with ease.

-Explorability Users can navigate throughout the UI without fear of penalty or unintended consequences, or of getting lost.
No frustration emotionally, users are satisfied with the interaction

2. How quickly and easily can the solution be implemented?

Does the solution offer an accelerated implementation approach to minimize demands on your resources? Rapid implementation techniques can reduce costs by more than 50 percent – again, this takes us back to the subject of Agile methodology.

3. How easily can the solution integrate with your supply chain, product development, and business processes?

No system operates in a vacuum, and it delivers the most value when embedded in the business! Your solution should have multiple points of integration, so that all business processes are outfitted with historical data in order to discern insights and take action.

4. Can the solution easily scale as your business grows?

Change. The only thing that remains constant. Take into account not only number of users, but also specific roles and functions and the need to support end-to-end business processes, which are constantly changing.

5. Is the business solution available as SaaS or subscription?

You can’t always anticipate your future, so being fiscally conservative is important. On-demand business solutions are often available on a subscription basis, virtually eliminating the traditional upfront investments. Alternately, if cash is a major issue, your solution provider should offer you some flexibility in billing, payment, intellectual property, and ownership – allowing you to keep your cash working while you get the benefits of the newest business technology solutions.

6. Does the solution offer you company-wide visibility into your business processes?

Link up with a solution provider who understands business process management. The right solution can help you gain a HUGE competitive advantage through increased visibility into critical business functions, superior reporting, integrated processes, and even increased customer loyalty/retention, more in-depth customer insights, and an accelerated product time to market.

7. Are there ample resources to assist you with your implementation and ongoing support?

Look for business partners with both long-term business experience and support services, as well as expertise with cross-functional, strategic, technology and software solutions.

8. Is industry-specific expertise built into the product?

The best business solutions are not plain vanilla.Your solution provider should understand your industry as well as you, and address any industry-specific needs, support roles, and functions unique to your vertical markets.

9. Does it provide you with any real-time monitoring and analytics?

by Michael Davison

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Insights

Madison Avenue Manslaughter : An Interview With Author Michael Farmer

The rise-and-conquer story of the advertising industry after the end of World War II has become woven into the fabric of modern American folklore: ads and commercials from the Golden Age of advertising (1945-1975) are forever etched in Baby Boomers’ memories, while the industry’s Mad Men themselves have been celebrated and further mythologized in our entertainment. The ad agency exec archetype, with his swagger and his 3-martini lunch, is one of the most familiar characters in American culture, while those actual Mad Men of the Golden Age, who pounded their concepts of “Big Ideas”, “Creativity”, and “Unlimited Service” to their clients, established such a mark upon advertising agency culture that it pervades the industry to this day, and remains the template for today’s advertising.

The problem with this, according to Michael Farmer, Chairman of Farmer & Company LLC, a strategy consulting firm for advertising agencies and advertisers, is that the industry has been turned completely on its head since the Golden Age, and the paradigms that were then in place then cannot address the state of the industry today. Peril is close at hand:

Today’s Mad Men celebrate new clients and creative awards just like the Mad Men of yesteryear, with champagne, parties and laudatory speeches, but the resemblance and the fun stop there. Returning to their daily routines, ad agency people put on a brave face, struggle with increasing workloads and demanding clients, and feel like players on a losing team, unable to break out or at least pull even with their clients as respected, secure partners. The advertising business, which was once one of the most fulfilling and glamorous of industries has become a grim sweatshop for the people who do the work.

The system is broken, says Mr. Farmer, and the ad industry is in dire straits. His riveting new book Madison Avenue Manslaughter recounts the “dizzying heights” of the Mad Men days, and tracks a timeline of the key events and technologies – such as remuneration changes, globalization, new ownership, shareholder value, and digital and social media – which brought about the weakening health of today’s advertising agencies, and are now typified by ever-growing and unaccountable workloads, reduced client fees, and shortened or one-off client engagements.

With a richly depicted history and a candid, thorough examination of the current state of advertising agencies, Madison Avenue Manslaughter lays out a detailed 10-step transformation program for those progressive industry CEOs who want to “restore organizational health, financial well being and renewed strategic relevance for their ad agencies”.

I recently had a short conversation with Michael Farmer, where we discussed Madison Avenue Manslaughter and mused about the future of the advertising industry.

Michael, first let me commend you on your book. As an advertising industry outsider, the setup of your argument– the comprehensive history and explanation of the current state of affairs– was so richly detailed, it felt like a page-turner. I learned quite a lot; the theme of your book brought to my mind a quote by Albert Einstein that I think is quite applicable to what you are describing:

We can’t solve problems by using the same kind of thinking we used when we created them.

How does this resonate in your mind with regards to what you describe in your book?

It’s hard to argue with Einstein! Yet, the mystery of the ad agency business is that executives are wedded to the concepts that created success in the period 1960 through 1980 — even though the conditions that allowed this past success do not exist. For example, agencies still believe that “highly creative TV ads drive client brand sales.” Well, that was true when TV was a novelty, as it was in the ‘60’s and ‘70’s, and amusing ads were a new thing….but today? TV ads are no longer a novelty, and we’re familiar with all the cliches and attempts to amuse. We’ve each digested several hundred thousand ads since that day, and we’re sick of them! Pure creativity is not the formula for success. Furthermore, agencies are paid 1/3rd what they used to be paid, so they can’t afford “full service.” Let’s face it, the world has changed, but they’re stuck in the past.

Agency remuneration in the Golden Age was commissions-based. Can you briefly describe the shift to today’s model, and what effect this has on workload?

Agencies then received 15% of their clients’ spend on media — for TV, radio and print. That covered ad creation. How much work they did was irrelevant to how much they were paid. In the ‘90s, though, most of the industry was required to change to “fee-based” remuneration, which means they are paid for the number and cost of people who work on a client’s account, plus some additional money for overhead and profit. This should correlate with the amount of work they do, but in fact it does not! Clients and agencies agree on fees and agency headcounts / fees, but nowhere in the system do they clearly spell out how much work is to be done and how many people it actually requires to get it done. This is a holdover from the “full service” days when remuneration was based on commissions. The new system, then, allows clients to grow the workloads but hold the agency fees and resources (people) constant, and that’s what happens. Workloads grow, but fees and resources are driven downwards. More work, fewer people. A complete disaster, and it continues every day!

You make an observation in your book that even as SOW communication happens after-the-fact, creative workloads skyrocket, unmeasured and completely independent of agency resources or fees, the typical agency C-level indeed does NOT want to know about or address this issue. Can you elaborate on the managerial passivity that pervades the industry, and why that is the case?

The passivity is irresponsible, in my view. Agency CEOs are not doing enough to ensure that their agencies are paid for all the work they do. They are reluctant to throw their weight behind “SOW tracking systems” that would be updated regularly by their senior client heads, and they absolutely are uninterested in reviewing client head performance — finding out who is giving away work and who is not. I can’t understand this, but it appears that they don’t really want to manage their organizations. They want to win new business and be viewed as creative geniuses, but they have little appetite for the hard work of management.

Your consultancy has built a database of Scope of Work (SOW) briefs, and has established a metric for measuring workload across them: the ScopeMetric(R) Unit, or SMU. Can you briefly explain the metric, how your organization uses it, and why it’s important?

Early in my consulting career with agencies, I found that I needed three things to understand agency operations: 1) the amount of work they were doing for each client; 2) their fees by client; and 3) the resources they allocated to each client. This is simply logical: “what are you doing for each client; how much are they paying you; how many people does it take.” Across an agency office of 20 clients, there would surely be “good clients” and “bad clients,” where the alignment among workload, fees and resources was out of whack. I needed to identify those situations. In order to do so, I had to figure out how to measure workload. Today, there’s a huge difference among the relative sizes of a TV ad, a print ad, a Tweet, and an online ad banner. I decided to use creative manhours as my basic measurement, using them to measure the size of different deliverables, categorized by media type (i.e., TV), media detail (TV:30), origination versus adaptation, and according to creative complexity (low, average, high).

I now have a database of about 7,000 deliverables, each with a unique SMU value based on creative manhours. The use of an SMU allows me to calculate “price” (fees divided by SMU workloads), “productivity” (SMUs per creative person per year) and other metrics.

Advertising agencies do not have a system in place for measuring workloads. Do you know of any turnkey “workload management platforms”, SOW measurement and management tools, or other solutions on the market today? Are there any early adopters?

Advertisers, on their own, have used systems like Decideware to keep track of agency deliverables, but even Decideware does not have a way of measuring the amount of work in the SOWs, We may team up with them to combine Farmer metrics with their system. Agencies, though, are resisters.

So what can be done? What is the ‘next best step’ that agency C-level management can take, right now?

If I were an agency CEO today, my first step would be to announce a policy. It would sound something like this:

Every client that we serve will have its SOW documented in a uniform way, using an agency-wide SOW tracking system using Farmer’s SMU metrics. Every client head will ensure that his / her SOWs are kept up to date in the tracking system.. Every quarter, we will review client performance, client-by-client, examining the alignment of client workloads, fees and agency resources. Clients whose workloads, fees and resources are misaligned in some way — like ‘too much work and too little fee’ — will require corrective action by client heads. We will review client head performance in correcting misalignments. Needless to say, it is imperative for our agency to be paid correctly for all the work we carry out, and for the resources required to carry out the work.

Michael, your book is currently for sale, and provides readers with a detailed cross-section of the operations and financials of a model agency, as well as a 10-step transformation program for CEOs. Are there any other resources, online or otherwise that you would recommend to your audience?

I write a blog from time to time, and it is published on http://farmerandco.com. The blog is a place where I can comment on developments in the industry associated with the under management of SOWs and agency remuneration. I try to make this an interesting resource — let me know how well I’m doing!

Madison Avenue Manslaughter is the winner of the 2016 Axiom Awards Gold Medal for Marketing Books, and is available online and in selected bookstores nationwide.

By Michael Davison

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Neural Networks: What They Are, and Their Many Applications

It’s behind the Tesla autopilot feature. It’s your recommendations from Netflix. It’s when Siri recognizes your speech and serves you results. It’s the foundation for your credit card’s fraud detection technology. We see the application of neural networks and machine learning all around us today in nearly every aspect of life.

With the exponentially increasing volumes and varieties of data, the advent of cheaper and faster computational processing, and ubiquitous affordable mass data storage, neural networks aren’t just for Google and Microsoft anymore. It’s important for small and medium business owners to know what neural networks are, what they can do for their business, and also what their limitations are.

So What is a Neural Network?

Let’s grab a definition from Dr. Robert Hecht-Nielsen, an early pioneer in neural networks in the 1980s and 1990s. He defines an artificial neural network (ANN) as:

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.”

An ANN mimics certain features of the brain’s physical structure and information processing, with a web of neural connections that consist of myriad interconnected and layered simple processing elements. Akin to its biological sibling, in an ANN:

1. Each processing element (essentially a neuron) receives inputs from other elements
2. The inputs are weighted and added,
3. The result is then transformed (by a transfer function) into the output.

The transfer function may be a step, a sigmoid function (S-curve), or hyperbolic tangent function, among others.

ANNs are basic learning devices in the form of hardware or software. In both cases, the fundamental idea is to assemble several single simple processors that interact through a dense web of interconnections, which result in a network architecture that is unlike the sequential linear processing and architecture of conventional computer systems.

How do Neural Networks Differ from Conventional Computers?

Conventional computers are good at numerical computation; they apply formulas, decision rules, and algorithms instructed by users to produce outputs from the inputs. A neural network, on the other hand, is not a general-purpose problem solver. It is good at complex numerical computation for the purposes of solving system of linear or non-linear equations, organizing data into equivalent classes, and adapting the solution model to environmental changes. But it is not good at such mundane tasks as calculating payroll, balancing checks, and generating invoices. Nor is it good at logical inference – a job suited for expert systems. Therefore, business leaders must know when a problem could be solved with an ANN; moreover, to make an ANN work, it must be tailored specifically to the problem it is intended to solve.

ANNs, like people, learn by example. They improve their own rules; the more decisions they make, the better the decisions may become. Data scientist and entrepreneur Jeremy Howard describes this phenomenon quite colorfully:

The difference here is each thing builds on each other thing. The data and the computational capability are increasing exponentially, and the more data you give these deep-learning networks and the more computational capability you give them, the better the result becomes because the results of previous machine-learning exercises can be fed back into the algorithms. That means each layer becomes a foundation for the next layer of machine learning, and the whole thing scales in a multiplicative way every year. There’s no reason to believe that has a limit.

An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Think of it simply as a branch of statistics, designed for a world of big data, where the most common application of machine learning is to make predictions.

Applying Neural Networks to Different Industries

Because a neural network must be built and tailored specifically to the problem it is intended to solve, you can’t just slap on a machine learning solution someone else did for their own context and set of data. The best way to determine if you can leverage neural networks in your own business and then reap the gains achieved by them is to learn and understand how neural networks intersect and function across a breadth of different industries; this will inform your own specific situation. I’ve shared several examples for you below.

Marketing

In marketing, we identify customers likely to respond positively to a product or service, and target any advertising or solicitation towards them. Target marketing involves market segmentation, where we divide the market into distinct groups of customers with different consumer behavior. Neural networks are well-equipped to carry this out by segmenting customers according to basic characteristics including demographics, socio-economic status, geographic location, purchase patterns, and attitude towards a product.

Unsupervised neural networks can be used to automatically group and segment customers based on the similarity of their characteristics, while supervised neural networks can be trained to learn the boundaries between customer segments based on a group of customers with known segment labels, for example: frequent buyer, occasional buyer, rare buyer. Machine learning can save your organization both time and money by ensuring that you avoid contacting customers who are unlikely to respond. One study showed that neural networks can be used to improve response rates from the typical one to two percent, up to 95%, simply by choosing which customers to send direct marketing mail advertisements to. Neural networks can also be used to monitor customer behavior patterns over time, and to learn to detect when a customer is about to switch to a competitor.

Retail & Sales

Neural networks are excellent in the realm of sales forecasting, due to their ability to simultaneously consider multiple variables such as market demand for a product, a customer’s disposable income, population size, product price, and the price of complementary products. Forecasting of sales in supermarkets and wholesale suppliers has been shown to outperform traditional statistical techniques like regression, as well as human experts.

Another important area where retail and sales can benefit from neural networks is in shopping cart analysis, such as gathering and inputting information relating to which products are often purchased together, or the expected time delay between sales of two products.

Retailers can use this information to make decisions about the layout of the store: if shopping cart analysis reveals a strong association between products A and B then they can entice consumers to buy product B by placing it near product A on the shelves. If there is a relationship between two products over time, say within 6 months of buying a printer the customer returns to buy a new cartridge, then retailers can use this information to contact the customer, decreasing the chance that the customer will purchase the product from a competitor.

Banking & Finance

One of the main areas of banking and finance that has been affected by neural networks is trading and financial forecasting. Neural networks have been applied successfully to problems like derivative securities pricing and hedging, futures price forecasting, exchange rate forecasting and stock performance and selection prediction since the 1990s.

But there are many other areas of banking and finance that have been improved through the use of neural networks. For many years, banks have used credit scoring techniques to determine which loan applicants they should lend money to. Traditionally, statistical techniques have driven the software. These days, however, neural networks are the underlying technique driving the decision making. Credit scoring systems can learn to correctly identify good or poor credit risks. Neural networks have also been successful in learning to predict corporate bankruptcy.

Insurance

The insurance industry can leverage neural networks in a similar means as the marketing industry: policy holders can be segmented into groups based upon their behaviors, which can help to determine effective premium pricing. And like the banking and finance sectors, the insurance industry is constantly aware of the need to detect fraud – neural networks can be trained to learn to detect fraudulent claims or unusual circumstances. Competition is fierce in the insurance industry, and when a policy holder leaves, useful information can be determined from their history which might indicate why they have left. Using machine learning to manage the offering certain customers incentives to stay, like reducing their premiums, or providing no-claims bonuses, can help to retain good customers.

Telecommunications

Machine learning offers telecommunications organizations a clear opportunity to ascertain a much more complete picture of their operations and their customers, as well as to further their innovation efforts. Some companies are using a series of neural networks to analyze customer and call data in order to predict if, when, and why a customer is likely to leave for another competitor. Many telecommunications organizations use machine learning to help predict the effects of forthcoming promotional strategies, as well as sift through and refine data to find the most profitable customers.

Other uses of neural networks in telecommunications include:

– Optimizing routing and quality of service by analyzing network traffic in real time

– Analyzing call data records in real time to identify fraudulent behavior immediately

– Allowing call center reps to flexibly and profitably modify subscriber calling plans immediately

– Tailoring marketing campaigns to individual customers using location-based and social networking technologies

-Using insights into customer behavior and usage to develop new products and services

Operations management

Neural networks have been used successfully in operations management, particularly in the areas of scheduling and planning. R&D regarding the scheduling of machinery, assembly lines, and cellular manufacturing using neural networks has been increasingly prevalent over fifteen years. Other scheduling problems, like timetabling, project scheduling, and multiprocessor task scheduling have also been addressed with neural networks.

The use of neural networks in various operations planning and control activities cover a broad spectrum of application, from demand forecasting, to shop floor scheduling and control. Neural networks have also been used in conjunction with simulation modeling to learn better manufacturing system design.

Operations management also benefit from neural networks in the area of quality control, as neural networks can be integrated with traditional statistical control techniques to enhance their performance. Examples of this include a neural network used to monitor soda bottles to make sure each bottle is filled and capped properly.

Neural networks can also be used in diagnostics, and have been used to detect faults in electrical equipment and satellite communication networks. Project management tasks have also been tackled by using neural networks to forecast project completion times for knowledge work projects, or to predict workloads and delivery times in software engineering and development projects.

Conclusion

It’s apparent that the application of neural network technology is having disruptive effects and is becoming more and more pervasive in common business operations – even across the SMB market – with every passing day. My advice for business leaders is to take the time to thoroughly learn and understand what the technolgy implies, so you may begin to identify use cases and scenerios within your own business ecosphere. Research and identify the possible opportunities or insights that may be gleaned by plugging into Big Data and/or employing data vendor systems. And by all means, seek the consultation of an expert – one that seeks to highlight and understand the key critical functions of your business, that identifies what parts and/or interaction points you need to improve, and then helps you articulate a realistic, cohesive plan to develop a scalable solution.

Sources

Chartiere, Tim. Big Data: How Data Analytics Is Transforming the World.
Lee, Eldon. Artificial neural networks and their business applications.
Smith, Kate and Jatinder, Gupta. Neural networks in business: techniques and applications for the operations researcher.

By Michael Davison

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5 Criteria for Prioritizing Business Optimization Projects in the Digital Space

“Going digital” – the globally pervasive term for the vague process of improving elements of your business with the fruit of new technology, such as advanced cloud-based analytics and machine learning solutions. Wireless communication is common these days, while cheap sensors, mobile devices, and other hardware enable businesses to direct and process oceans of data, manage people and equipment remotely, and boost the efficiency of field service personnel.

Clearly, digital can reshape any aspect of modern enterprise. Consider QM, an iPad application that True Interaction built for Lifetime Brands. QM enables organizations to oversee and manage Quality Control Inspections in real time, regardless of where factories, product lots, or distribution points are located. QM conducts multiple inspection types, including factories, products, and social compliance, powered by SAP cloud data.

I’ve written before regarding the huge gains reaped by progressive businesses in the digital space. The race is on, but where should you start? What aspect of the business should come first? Sales? Customer Service? Logistics? Procurement? Planning? Production? More importantly, how can business leaders prioritize their company’s improvement in the digital space?

Evaluation Criteria

Obviously, companies should select the most relevant and useful “digital solution” that creates the most value and/or best addresses gaps in productivity or performance, but this isn’t always clearly indicated. I have gathered 5 criteria that any “digital solution” should be evaluated against in order to aid you in establishing the priority of what needs to be done:

1. Business Case

Every idea on the table for implementation should have a well-developed business case – a justification for the proposed project or undertaking on the basis of its expected commercial benefit – such as the potential boost in sales and decrease in costs or inventory, for example. Prioritize the opportunities that seem the most realistic, relevant, and financially rewarding.

2. Pain Points Addressed

Describe and list every pain point that each solution will address. Evaluate the respective pain points from both a quantitative and qualitative perspective.

3. Technological Feasibility

Assess the economic competitiveness of all of your proposed tech solutions by evaluating their implementation costs for improving a process, as compared to the costs incurred by the current technology.

4. Ease of Implementation

Some ideas may have potential to make a significant improvement to your business, however there may be associated roadblocks and hindrances, such as significant capital investment, or approval requirements from a board or external parties. In cases like this, the improvement cycle may be delayed, or the spirit of the implementers broken as the improvement activity becomes too difficult to implement. Likewise, if there are improvement opportunities which are easy to implement but don’t really make a difference, then team members may see the process as not providing much benefit, and once again could lose interest in the process.

5. Time to Impact

Some solutions have significant impact on your business as soon as the “flip is switched” – such as mobile productivity apps and cloud-based repositories. Other solutions may require a significant ramp-up time before their impact on your business is tangible. For example, certain Machine Learning algorithms require a considerable dataset in place before they become effective. It’s important to take this into consideration when evaluating your solution.
Reflection

If you take the time to thoroughly evaluate all of your organization’s ideas and technology solutions across the same spectrum of criteria, you will find that prioritizing what needs to be done becomes much less of a headache. You will also reap other benefits as well, such as having the ammunition at hand to wrangle consensus from your organization’s key stakeholders on what the next best digital step will be. Good luck!

By Michael Davison

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How can Your Business Optimize B2B Sales?

The Challenge

Business to Business (B2B) sales can be an incredibly nebulous, complicated, and uncertain arena to navigate and manage. The products may be varied and complex, the sales cycles are wide-ranging, and many decision-affecting influencers, contingencies, and persons/agencies may be involved.

Compete in B2B at the global level, and your challenges compound exponentially. Consider the situation of Lifetime Brands, a leading global provider of kitchenware products that include household brands such as Farberware®, KitchenAid®, Mikasa®, Pfaltzgraff®, Built®, and Fred and Friends®.

Cliff Siegel, EVP of Global Supply, at a Lifetime Brands warehouse in NJ.

Encompassing a vast, global network of factories, production schedules, warehouses, distribution points, demand planning systems, budgeting processes, and scheduling, you can imagine that Lifetime Brands’ B2B sales ecosystem is both complex and ever-changing. The sheer size and breadth of the product line makes the sales ecosystem especially vulnerable to issues and challenges that include prohibitive lead times, continually changing or unclear data, redundant manual tasks and interactions, difficult to manage budgeting processes, and difficulty in clearly aligning budget to strategic plan, among others.

Taking Action

Exactly how can a company sidestep or conquer these perils? Lifetime Brands reached out to True Interaction to help mitigate the inherent discord typified by complex sales operations. Cliff Siegel, the visionary EVP of Global Supply Chain at Lifetime Brands, had championed a set of core goals for his organization that should be familiar to all business leaders: Lifetime Brands wanted to grow revenues and increase profitability. They wanted to attract new customers, and keep existing customers happy. They wanted to make Sales operations as efficient and effective as possible. And they wanted to be able to identify and track opportunities and threats within their sales ecosystem.

Mr. Siegel knew that in order to attain these goals, Lifetime Brands would need to assume a proactive, progressive stance and shape their circumstances rather than react to them. They would need to true up the core engine of their business – the Platforms, Processes & People within their sales ecosphere – in order to transform a patchwork of disparate applications and methods into a harmonious, unified entity.

Liam Wright, CEO and Innovation Specialist of True Interaction, teamed up with Mr. Siegel to conduct an exhaustive bottom-up inventory of the existing system and all external data points and data sources, such as Demand Solutions and SAP. The disparate pieces were gathered, identified, organized, defined (and refined) using True Interaction’s proprietary innovation process including several rounds of whiteboard sessions and data identification meetings. Based upon this process, Matt Tidd, TI Chief Technical Director, had a complete map of the information landscape, and was able to architect a full-stack custom solution for Lifetime Brands, complete with the appropriate features and specifications; including Milestones, User Stories, and Tasks laid across a well-articulated development path.

A Solution

The product outcome is a fully articulated and unified Sales Portal platform, encompassing both an operational planning component and a financial planning component, that can provide sales estimates based on past history as well as the numerous continuous factors, events, and data points that influence day-to-day operations and financials. As a result, the entire value-added planning process accepts revisions quickly and easily. The Sales Portal system is able to link planning and planning procedures to the strategic plan, minimizes the time spent gathering data, maximizes the time for strategic decision making, automates collections and consolidation of budgets, enables collaboration, tracks sentiment, provides various level managers with fiscal control, and establishes a data warehouse for insightful financial planning and reporting, all within a beautiful user-experience. But most importantly, True Interaction built a custom product for Lifetime Brands that is not only cost-effective to implement, but also saves Lifetime Brands hundreds of thousands of man-hours in future enterprise operations.

“Sales portal allows us to simplify and focus the sales process, and ensure the lines of communications between sales and marketing teams are in perfect harmony. Additional benefit is gained from the resulting structured data that we can use to make informed decisions on what our customers are looking for and to better equip our sales team,” says Cliff Siegel. By investing in technology and implementing the proper digital solution, Lifetime Brands has amplified both financial and human value for its organization.

Now that Lifetime Brands has harnessed all of these disparate data sources into a unified system, they are also reaping serendipitous, unplanned-for benefits as well. For example, based upon the collaborative sales projections that the system now provides Lifetime Brands, the organization is now able to more accurately plan the related inventory requirements throughout the calendar year.

Reflection

Is the B2B sales process in your business well-organized? How well does your organization wield technology to achieve their better achieve their goals? According to SMB Group’s 2015 SMB Routes to Market Study, 29% of small-to-medium businesses (SMBs) view technology as helping them to significantly improve business outcomes. These “progressive” SMBs are 18% more likely to successfully forecast revenue increases than their peers. Progressive SMBs spend 29% more on technology, are 55% more likely to have fully integrated primary business applications (financials, CRM, HR, etc.) and are 86% more likely to use analytics than their counterparts.

 

By using technology to streamline workflows, slash time spent on repetitive data entry and inefficient processes, gain better insights into opportunities and threats, and create new business models, progressive SMBs are well positioned to tap into new customer requirements, improve customer engagement and experience, and enter new markets. As they move forward, they will continue to outpace their peers and reshape the SMB market. ~Laurie McCabe, Partner at SMB Group, Inc.

 

Are you able to make informed, actionable business decisions? Is your organization evolving with our digital ecosphere as it expands and develops? It’s time to invest in the future.

By Michael Davison

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Do Your Company’s Processes Contribute to Creating Value?

As I wrote in my post last week, data shows that for SMBs, the race is still on to develop and realize a true digital business ecosphere. Ultimately, the core of any business is composed of a system of Platforms, Processes, and People. When this system is not flexible enough to accommodate new business demands – or there is a bottleneck in the flow of inputting, receiving, or processing and taking action on data – the frameworks in place force users and customers to modify their behaviors and act according to contrived rules that are based upon weaknesses and gaps in process, rather than in harmony with the environment at large.

The roadmap to creating and improving value in your organization starts with discovering and understanding these weaknesses and gaps. If you bring cracks and imperfections to light, never fear! Business process influencer Keith Swenson succinctly put it this way in his podcast, “It is ironic, that to make a robust reliable system, you do so not by hiding problems, but by exposing all the problems as they happen.”

So how do your business processes stack up?

Reviewing your processes

Let’s get started. Robert Glushko and Tim McGrath have assembled some high-level questions that can drive the initial conversation, such as:

– What is the name of the process?
– What are the goals or purposes of the process?
– What industries, functional areas, or organizations are involved in the process?
– Who are the stakeholders or participants in the process?
– Are there any problems with the current process?
– How could the process be improved?

“Increased awareness of your processess dimensionality, invariably leads to an intuitive understanding of its Strengths, Weaknessess, Opportunities & Threats.”

Go both ways

These of course, are just the inaugural questions of a true process audit. Your quintessential goal is to identify and determine what the business does, in a hierarchy of detail from the topmost level, all the way down to where individual documents (and specific information components in document exchanges) are visible. To truly understand these processes, we need to examine this from both the top-down and bottom-up points of view. This ensures understanding and confluence of strategic focus, as well as the requirements for granular tasks and contingencies.

With a little Google-fu, you can find a wealth of resources for your process review. I like this handy comprehensive list of questions to improve processes published by the University of Michigan.

Also, check out Janne Ohtonen’s 12 Important Questions When Starting BPM Projects.

Where should I start?

I’m going to assume that you are not Google, and your company does not have unlimited resources. Examine all areas and departments of your business so you can determine the most efficacious route to the biggest dividend in improvement. What small changes can you make that will have the most effect? What would your ideal solution look like? Is it aligned with your business goals? How much time and capital will your business need to invest? It’s not enough to just identify processes for improvement; you need a feasible and reasonable plan to achieve that improvement. I’ll expand on this and include tips and methodologies on this subject in future posts.

The time is now

My post on last Friday underscored the fact that technology is becoming increasingly essential to modern business, and that those SMBs that make the technology leap will reap tremendous gains over their less progressive peers. With that in mind, ALL SMBs should take the time to reassess the value of their business processes and technology solutions on a regular basis. As TI CEO O. Liam Wright says, “Increased awareness of your processes’ dimensionality invariably leads to an intuitive understanding of its Strengths, Weaknesses, Opportunities & Threats.”

By Michael Davison

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Is Your IT Department Ready for the Digital Age?

If you are smiling to yourself about the title of this post, and the quaint term “Digital Age,” and how it’s 2016 already, and the “Digital Age” has been upon us for years now, you may want to stack your SMB up against a few eye-opening metrics regarding the state of technology in small-to-medium businesses today.

Big data is upon us, and available to every enterprise, including small businesses – but it’s what you do with it that counts. The International Data Corporation (IDC) forecasts a 44-fold increase in data volumes between 2009 and 2020. Despite this cambrian explosion of data, SMBs still appear to be behind when it comes to their IT capability. IDC projects a 40% growth in global data per year vs. just 5% growth in global IT spending in the future; furthermore, the organization noted that a shocking 68% of companies do not have a stated Business Intelligence / Analytics Strategy, and 79% of SMBs still use manual integration such as manual Excel files, or custom code.

Companies are no longer suffering from a lack of data—they’re suffering from a lack of the right data. Business leaders need the right big data to effectively define the strategic direction of the enterprise. The current generation of software was designed for functionality, but the next generation must also be designed for analytics. ~ Accenture Business Technology Trends Report

Right now amongst progressive SMBs, the race is on to develop and realize a true digital business ecosphere. Progressive SMBs are 55% more likely to have fully integrated business applications. Where does your organization stand in this contest? IDC predicts that by 2017, the transfer of cloud, social and big data investments from IT to line-of-business budgets will require 60% of CIOs to focus the IT budget on business innovation and value. This metric jibes with the results from Accenture’s Technology Vision survey, polling more than 2,000 business and technology executives across nine countries and 10 industries. According to the survey, 62% of SMBs are currently investing in digital technologies, and 35 percent are comprehensively investing in digital as part of their overall business strategy. In a recent article, Laurie McCabe, Partner at tech industry research masterminds SMB Group, points out that that these progressive SMBs are well positioned to tap into new customer requirements, improve customer engagement and experience, and enter new markets. “As progressive SMBs move forward,” she notes, “they will continue to outpace their peers and reshape the SMB market.”

How will the market be reshaped? Accenture notes that 81 percent of companies believe that “in the future, industry boundaries will dramatically blur as platforms reshape industries into interconnected ecosystems”. Progressive SMBs that continue to invest in IT capability will reap tremendous gains; those that bring up the rear will be behind by orders of magnitude. Furthermore, being progressive leads directly to revenue: For instance, according to SMB Group, 75% of the Progressive medium businesses (who increased technology spending) anticipated revenue gains in 2012, compared to just 17% of medium businesses that decreased IT spending.

Naturally, some SMBs don’t have the budget or staff to “flip the switch” and implement a bottom-up overhaul of their entire business process to create a fully integrated digital solution. But that is OK. By working with a capable digital business enterprise development vendor, SMBs can commence an incremental, but still integrated approach to business management solutions. Companies can begin, for example, with a financials module, and then continue to add integrated modules as required and when able, to manage other functions such as manufacturing, distribution, project accounting, or sales and marketing, at their own pace. The important part is to get moving, and to take the time to honestly assess how your organization is using technology today.

True Interaction produces custom full-stack end-to-end custom secure and compliant technology solutions across web, desktop and mobile – integrating data sources from e-commerce, enterprise resource planning, customer service, document inventory management, spend, performance… whatever data your business requires. From legacy systems to open source, we can determine the most optimal means to achieve operational perfection, devising and implementing the right tech stack to fit your business. We routinely pull together disparate data sources, fuse together disconnected silos, and do exactly what it takes for organizations to operate with tight tolerances, making your business engine hum.

Are you ready for the Digital Age? For real, this time? Let’s go!

By Michael Davison